feat: publish dagger-py-functions.zip to github packages

.github/workflows/package.yml

@@ -29,3 +29,7 @@ jobs:
         run: ./gradlew :dagger-core:minimalAndShadowPublish
         env:
           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+      - name: Publish Zip Package of dagger-py-functions
+        run: ./gradlew publishPyZipPublicationToGitHubPackagesRepository
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}

build.gradle

@@ -1,7 +1,25 @@
+buildscript {
+    repositories {
+        mavenLocal()
+        mavenCentral()
+        maven {
+            url "https://plugins.gradle.org/m2/"
+        }
+    }
+
+}
+
+plugins {
+    id 'maven-publish'
+}
+
 ext {
     flinkVersion = System.getenv('flinkVersion') ?: '1.14.3'
 }
 
+def pyZipVersion = rootProject.file('version.txt').text.trim()
+group 'io.odpf'
+
 subprojects {
     repositories {
         mavenLocal()
@@ -15,10 +33,37 @@ subprojects {
     apply plugin: 'idea'
     apply plugin: 'checkstyle'
 
-    group 'io.odpf'
-
     checkstyle {
         toolVersion '7.6.1'
         configFile rootProject.file("config/checkstyle/checkstyle.xml")
     }
+}
+
+task makePyZip(type: Zip) {
+    from fileTree(dir: 'dagger-py-functions')
+    include '**/*'
+    archiveName "dagger-py-functions.zip"
+    destinationDir file("$rootDir/")
+}
+
+publishing {
+    publications {
+        pyZip(MavenPublication) {
+            artifact source: makePyZip, extension: 'zip'
+            groupId group
+            artifactId 'dagger-py-functions'
+            version pyZipVersion
+        }
+    }
+
+    repositories {
+        maven {
+            name = "GitHubPackages"
+            url = "https://maven.pkg.github.com/odpf/dagger"
+            credentials {
+                username = System.getenv("GITHUB_ACTOR")
+                password = System.getenv("GITHUB_TOKEN")
+            }
+        }
+    }
 }

dagger-py-functions/udfs/scalar/sample_udf.py

@@ -0,0 +1,9 @@
+from pyflink.table import ScalarFunction, DataTypes
+from pyflink.table.udf import udf
+
+class SampleUdf(ScalarFunction):
+
+    def eval(self, text: str):
+        return text + "_added_text"
+
+sample_udf = udf(SampleUdf(), result_type=DataTypes.STRING())